ProcessDeliveryAnalysis

Load data, baltimore city stats, and do a join

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggmap)

## Loading required package: ggplot2

#default.data <- read.csv("defaultCasesOverServerAndZipcode.csv", sep="\t")
default.data <- read.csv("defaultCasesOverServerAndZipcodeModifiedCaseCriteria.csv", sep="\t")
default.data$DefendantZip <- as.factor(default.data$DefendantZip)
service.data <- read.csv("serviceStatsOverServerAndZipcode.csv", sep="\t")

bmore.stats <- data.frame(
  zipcodes=factor(c(21207,21209, 21210, 21211,21212, 21213,21215,21214,21217,21216,21218,21223,21224, 21225,21226,21229,21231,21230 ,21239, 21251,21263,21287,21201,21202,21205)),
  med.income=c(52253, 73577, 83051, 57572, 65450, 32317, 34885, 63412, 27382, 33857, 37959, 26342, 56997, 37992,  65625, 44485, 57071, 67132, 50809, NA, NA, NA, 30853, 31638, 24187))

# for presentation, just show the relevant ones to paint our picture
bmore.stats <- bmore.stats[c(6,7,9,11),]

geo.coords <- read.csv("zipCodeGeoCoordLongLat.csv")
geo.coords$ZIP <- as.factor(geo.coords$ZIP)
bmore.stats <- inner_join(bmore.stats, geo.coords, by=c("zipcodes" = "ZIP"))

## Warning in inner_join_impl(x, y, by$x, by$y): joining factors with
## different levels, coercing to character vector

default.data <- inner_join(default.data, bmore.stats['zipcodes'], by=c("DefendantZip" = "zipcodes"))

## Warning in inner_join_impl(x, y, by$x, by$y): joining factor and character
## vector, coercing into character vector

default.data$DefendantZip <- as.factor(default.data$DefendantZip)
service.data <- inner_join(service.data, bmore.stats['zipcodes'], by=c("DefendantZip" = "zipcodes"))

## Warning in inner_join_impl(x, y, by$x, by$y): joining factor and character
## vector, coercing into character vector

service.data$DefendantZip <- as.factor(service.data$DefendantZip)

Plot an income map image

qmap("Baltimore, MD", zoom=12) + geom_point(data=bmore.stats, aes(x=LNG, y=LAT, color=med.income, size=med.income))

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=Baltimore,+MD&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Baltimore,%20MD&sensor=false

Visualizing defaulting stats

Plot the raw number of defaults on a map

default.result <- default.data %>% group_by(DefendantZip) %>% summarize(numberDefaultedCases = sum(numDefaults)) %>% inner_join(bmore.stats, by=c("DefendantZip" = "zipcodes")) %>% arrange(desc(numberDefaultedCases)) %>%  as.data.frame()

## Warning in inner_join_impl(x, y, by$x, by$y): joining factor and character
## vector, coercing into character vector

qmap("Baltimore, MD", zoom=12) + geom_point(data=default.result, aes(x=LNG, y=LAT, color=numberDefaultedCases, size=numberDefaultedCases))

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=Baltimore,+MD&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Baltimore,%20MD&sensor=false

For each zip code, find the percentage of cases that are a default settlement.

## Warning in inner_join_impl(x, y, by$x, by$y): joining factor and character
## vector, coercing into character vector

Plot the results

ggplot(data=default.result) + geom_line(aes(x=1:nrow(default.result), y=percentDefault))

Plotting percent default by median income of the region

ggplot(data=default.result) + geom_point(aes(x=med.income, y=percentDefault)) + labs(x="Median Income", y="Percentage of Defaulted Cases")

Plot the percentages on a map

qmap("Baltimore, MD", zoom=12) + geom_point(data=default.result, aes(x=LNG, y=LAT, color=percentDefault, size=percentDefault))

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=Baltimore,+MD&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Baltimore,%20MD&sensor=false

Table of highest overall number of civil cases in general:

library(gtable)
library(grid)
library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

default.res3 <- default.data %>% group_by(DefendantZip) %>% summarize(totalNumberCases = sum(totalNumCases)) %>% arrange(desc(totalNumberCases))


table <- tableGrob(default.res3, rows=NULL)
title <- textGrob("Test", gp = gpar(fontsize = 10))
padding <- unit(0.5,"line")
table <- gtable_add_rows(
  table, heights = grobHeight(title) + padding, pos = 0
)
table <- gtable_add_grob(
  table, list(title),
  t = 1, l = 1, r = ncol(table)
)
grid.newpage()
grid.draw(table)

Find the how the case resolutions are distributed across regions, and also how the default cases are distributed.

default.res2 <- default.data %>% group_by(DefendantZip) %>% summarize(totalNumDefaults = sum(numDefaults), totalNotDefaults = sum(totalNumCases) - sum(numDefaults)) %>% tidyr::gather("DefaultsResult", "Count", 2:3)
#ggplot(data=default.res2, aes(DefendantZip)) + geom_bar(aes(weight = totalNumDefaults)) + labs(x="Zip Code", y="Number of Defaulted Cases")


ggplot(data=default.res2, aes(x = DefendantZip, y = Count, fill = DefaultsResult)) + geom_bar(stat="identity") + scale_fill_discrete(name="Default Case Breakdown",labels=c("Case Not Defaulted", "Case Defaulted"))

Visualizing service stats

Show the percentage of processes that are actually served for each zip code.

## Warning in inner_join_impl(x, y, by$x, by$y): joining factor and character
## vector, coercing into character vector

ggplot(data=service.result) + geom_line(aes(x=1:nrow(service.result), y=percentServed))

Plotting area percentage of processes served as function of med income

ggplot(data=service.result) + geom_point(aes(x=med.income, y=percentServed)) + labs(x="Median Income", y="Percentage of Processes Served")